Google Machine Learning Engineer Interview Questions

I would keep the previous model version available and make routing versioned so traffic can move back quickly if something breaks. The serving system should support canary rollout, shadow traffic, and a fast switch to a stable model. The reason I choose this approach is that ML deployments need the same rollback discipline as software deployments, plus extra attention to quality and compatibility. I would also think about prompt compatibility, embedding changes, and downstream consumers. If the interviewer wants more detail, I would discuss rollback testing before full release.

I would first decide which feedback signals are trustworthy, because not all user reactions are equally useful. Then I would log inputs, outputs, and feedback in a privacy-safe way so the data can be reviewed and used later for training or evaluation. The reason I choose this approach is that model improvement depends on learning from real failures, not just benchmark data. I would also think about label quality, bias, and how to avoid overfitting to noisy feedback. If the interviewer wants more depth, I would describe a curation pipeline for turning feedback into training examples.

I would first define what 'best' means, because it could mean lower cost, higher quality, lower latency, or better safety. My design would include a router that looks at request type, user risk, latency budget, and model capability before selecting a target model. The reason I choose this approach is that not every request needs the most expensive model. I would also think about fallback logic, router evaluation, and how to avoid inconsistent behavior. If the interviewer wants more detail, I would mention per-request telemetry and A/B testing.

I would make sure the system has clear metrics, versioning, rollback, monitoring, and a reliable data pipeline. I would also confirm that the model is evaluated both offline and online and that there is a plan for drift, failures, and misuse. The reason I choose this checklist is that a strong model by itself is not enough; the surrounding system has to be dependable too. I would also think about privacy, safety, and cost before calling the system production-ready. In an interview, I would emphasize that production readiness is about the full pipeline, not just the model artifact.